{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Programming Exercise 8 - Anomaly Detection and Recommender Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Anomaly Detection](#Anomaly-Detection)\n",
    "- [Recommender Systems](#Recommender-Systems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load ../../../standard_import.txt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.io import loadmat\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "\n",
    "pd.set_option('display.notebook_repr_html', False)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 150)\n",
    "pd.set_option('display.max_seq_items', None)\n",
    " \n",
    "#%config InlineBackend.figure_formats = {'pdf',}\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('notebook')\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anomaly Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', 'yval', '__version__', 'Xval', 'X', '__globals__'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = loadmat('data/ex8data1.mat')\n",
    "data1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1: (307, 2)\n"
     ]
    }
   ],
   "source": [
    "X1 = data1['X']\n",
    "print('X1:', X1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PZN8PHhxs58m8Fnd2c8udtswnSoZca+bnzp3TxIkT1b59e0VHR2vGjBn65Zdf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\ncgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e6ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X1[:,0], X1[:,1], c='b', marker='x')\n",
    "plt.title(\"Outlier detection\")\n",
    "plt.xlabel('Latency (ms)')\n",
    "plt.ylabel('Throughput (mb/s)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EllipticEnvelope(assume_centered=False, contamination=0.1, random_state=None,\n",
       "         store_precision=True, support_fraction=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = EllipticEnvelope()\n",
    "clf.fit(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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25ZdffjF9XgkhhLBd1pnAWJH8amCMSULNmjXx9fXl7bffJj09nS+++AInJyd+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\n8hj/H9YuPZWjlvWJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcffbb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the grid for plotting\n",
    "xx, yy = np.meshgrid(np.linspace(0, 25, 200), np.linspace(0, 30, 200))\n",
    "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# Calculate the decision function and use threshold to determine outliers\n",
    "y_pred = clf.decision_function(X1).ravel()\n",
    "percentile = 1.9\n",
    "threshold = np.percentile(y_pred, percentile)\n",
    "outliers = y_pred < threshold\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14,5))\n",
    "\n",
    "# Left plot\n",
    "# Plot the decision function values\n",
    "sns.distplot(y_pred, rug=True, ax=ax1)\n",
    "# Plot the decision function values for the outliers in red\n",
    "sns.distplot(y_pred[outliers], rug=True, hist=False, kde=False, norm_hist=True, color='r', ax=ax1)\n",
    "ax1.vlines(threshold, 0, 0.9, colors='r', linestyles='dotted',\n",
    "           label='Threshold for {} percentile = {}'.format(percentile, np.round(threshold, 2)))\n",
    "ax1.set_title('Distribution of Elliptic Envelope decision function values');\n",
    "ax1.legend(loc='best')\n",
    "\n",
    "# Right plot\n",
    "# Plot the observations\n",
    "ax2.scatter(X1[:,0], X1[:,1], c='b', marker='x')\n",
    "# Plot outliers\n",
    "ax2.scatter(X1[outliers][:,0], X1[outliers][:,1], c='r', marker='x', linewidths=2)\n",
    "# Plot decision boundary based on threshold\n",
    "ax2.contour(xx, yy, Z, levels=[threshold], linewidths=2, colors='red', linestyles='dotted')\n",
    "ax2.set_title(\"Outlier detection\")\n",
    "ax2.set_xlabel('Latency (ms)')\n",
    "ax2.set_ylabel('Throughput (mb/s)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Recommender Systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__globals__', 'R', 'Y', '__version__'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = loadmat('data/ex8_movies.mat')\n",
    "data2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y: (1682, 943)\n",
      "R: (1682, 943)\n"
     ]
    }
   ],
   "source": [
    "Y = data2['Y']\n",
    "R = data2['R']\n",
    "print('Y:', Y.shape)\n",
    "print('R:', R.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 4, 0, ..., 5, 0, 0],\n",
       "       [3, 0, 0, ..., 0, 0, 5],\n",
       "       [4, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, ..., 1, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 1],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ezvUjWi+ljFeutpJTU3rDL745+t47/+4jyfIRFUmPzuquDY22/TwAbolKcuJo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e6dc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Y, yticklabels=False, xticklabels=False);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
